Search local businesses via Google Local: names, addresses, ratings, reviews, phone, website, coordinates. Price: $0.045
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results (default: 10, max: 20) | |
| query | Yes | What to search for (e.g. coffee shops) | |
| location | No | Free-text location bias, e.g. "Austin, Texas" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must bear the full burden. It does disclose the return fields (names, addresses, ratings, etc.) and the price ($0.045), which is useful. However, it does not mention rate limits, authentication requirements, pagination behavior, or any other operational details, leaving behavioral transparency incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the core purpose and then lists the returned data. The price is appended as a separate clause, adding practical value without wordiness. Every element earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (3 params, no output schema), the description covers the essential elements: what it does, what data it returns, and its cost. It lacks explicit guidance on default pagination or error handling, but these are not critical for a search tool. The description is reasonably complete for its scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-specific semantics beyond what the schema already provides (query, location, limit). It lists return fields but not parameter nuances, so it remains at baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('Search') and resource ('local businesses via Google Local'), and enumerates the returned fields (names, addresses, ratings, reviews, phone, website, coordinates). This distinguishes it from sibling search tools like search_web and search_news, which target broader or different content types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context by specifying 'local businesses via Google Local', which implies when to use it (when local business info is needed). However, it does not explicitly mention alternatives or exclusion conditions, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tool clusters have near-overlapping purposes: fetch_webpage/fetch_webpage_pro/fetch_resilient and batch_fetch/get_contents are hard to distinguish, and answer_question/research/deep_research differ mainly in price and depth. The search_* and intel_* families are clearer, but the core fetching and research overlap creates ambiguity.
Most tools follow a verb_noun snake_case pattern (fetch_webpage, search_web, extract_data), but there are notable exceptions like domain_intel, package_intel, youtube_transcript, memory_set, and intel_company, where the prefix/suffix convention is inconsistent. Still, the naming is broadly readable.
35 tools is a large surface, far beyond the typical 3-15 range. The server covers many research verticals, but the number feels bloated, especially with multiple fetch and research variants that could be consolidated.
The tool set covers a wide range of web research needs: searching, fetching, crawling, extracting, screenshots, domain/tech/package intelligence, and market/competitive analysis. It lacks obvious lifecycle operations for monitors (list/delete/update) and memory (get/delete), but core workflows are well covered.